• Advances in nano research
• /
• 제12권2호
• /
• pp.185-195
• /
• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• 디지털융복합연구
• /
• 제19권11호
• /
• pp.415-424
• /
• 2021

본 연구는 노년의 행복을 측정하기 위해 개발된 주관적 에이징웰 척도(Subjective Agingwell Scale: SAS)를 타당화하고자 하였다. 척도의 타당성을 검증하기 위해, 수도권에 거주하는 노인을 대상으로 주관적 에이징웰, 주관적 안녕감, 낙관주의, 지각된 통제력, 건강준수 행동을 측정하는 질문지를 실시하였다. 자료 분석에는 최종 342명의 응답결과가 사용되었다. 결과를 살펴보면, 첫째, 신뢰도 분석결과, 11문항의 SAS의 전체 및 하위 척도 신뢰도는 수용할만한 수준이었다. 둘째, 확인적 요인분석 및 상관분석 결과, SAS는 구성타당도가 있는 것으로 나타났다. 3요인(인지, 정서, 영) 구조의 적합도는 양호한 편이었고, 주관적 에이징웰과 주관적 안녕감, 낙관성은 정적 상관을 보였다. 마지막으로 회귀분석 결과, 주관적 에이징웰은 건강행동 준수를 예측하였고 준거타당도가 지지되었다. 본 연구는 주관적 관점의 에이징웰 측정도구를 국내 노인에게 적용할 수 있도록 타당화했다는 점에서 의미가 있다.

• 대한원격탐사학회지
• /
• 제38권4호
• /
• pp.343-353
• /
• 2022

An attempt to derive the surface temperature from the Korea Multi-purpose Satellite (KOMPSAT)-3A mid-wave infrared (MWIR) data acquired over the southern California on Nov. 14, 2015 has been made using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model. Since after the successful launch on March 25, 2015, the KOMPSAT-3A spacecraft and its two payload instruments - the high-resolution multispectral optical sensor and the scanner infrared imaging system (SIIS) - continue to operate properly. SIIS uses the MWIR spectral band of 3.3-5.2 ㎛ for data acquisition. As input data for the realistic simulation of the KOMPSAT-3A SIIS imaging conditions in the MODTRAN model, we used the National Centers for Environmental Prediction (NCEP) atmospheric profiles, the KOMPSAT-3Asensor response function, the solar and line-of-sight geometry, and the University of Wisconsin emissivity database. The land cover type of the study area includes water,sand, and agricultural (vegetated) land located in the southern California. Results of surface temperature showed the reasonable geographical pattern over water, sand, and agricultural land. It is however worthwhile to note that the surface temperature pattern does not resemble the top-of-atmosphere (TOA) radiance counterpart. This is because MWIR TOA radiances consist of both shortwave (0.2-5 ㎛) and longwave (5-50 ㎛) components and the surface temperature depends solely upon the surface emitted radiance of longwave components. We found in our case that the shortwave surface reflection primarily causes the difference of geographical pattern between surface temperature and TOA radiance. Validation of the surface temperature for this study is practically difficult to perform due to the lack of ground truth data. We therefore made simple comparisons with two datasets over Salton Sea: National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) field data and Salton Sea data. The current estimate differs with these datasets by 2.2 K and 1.4 K, respectively, though it seems not possible to quantify factors causing such differences.

• Imaging Science in Dentistry
• /
• 제52권4호
• /
• pp.351-357
• /
• 2022

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion: This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

• International Journal of Computer Science & Network Security
• /
• 제23권8호
• /
• pp.17-25
• /
• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• 대한물리의학회지
• /
• 제18권3호
• /
• pp.121-133
• /
• 2023

PURPOSE: Computerization using ICF as a protocol can enhance the assessment, communication, and decision-making of various disciplines and cultures, individual functions, disabilities and health to promote communication and understanding among various professionals, organizations, and countries. The empirical foundation for these propositions was provided by delineating of six distinct computerization components. METHODS: This study analyzed 14 papers that combined the medical field and information technology to activate the ICF through computerization. From each of these papers, distinctive advantages were extracted to propose six computerization elements. The validity of these computerization elements was examined. These papers encompass various computerization elements, among which core elements were identified. In particular, six common core elements were extracted from these papers and assumed to be strategic computerization components for ICF activation. A heuristic methodology was employed to validate these components, representing IT technology maturity using four determining indices, which were then presented graphically for validation attempts. RESULTS: Four quantified indices were defined: reliability, cost-effectiveness, support and updates, and collaboration. Using these indices, this study identified elements that leverage existing IT technologies and require new development. The possibility of increasing utility was identified by applying computerization to ICF. CONCLUSION: This study examined the strategic elements of utilizing ICF by computerizing it using a protocol concept and discussed its potential for utilization. The potential to enhance the value of information in social, physical, and cultural contexts was presented by integrating various domains and data within the ICF framework.

• 한국지반공학회논문집
• /
• 제26권2호
• /
• pp.33-43
• /
• 2010

지열은 고효율 신재생에너지로 각광을 받고 있으며 건축물의 냉난방 설비 시스템으로 활용이 점차 확산되고 있다. 지열 히트펌프 중에서 지하수를 열원으로 사용하는 단일심정(Standing column well)은 특히 효율이 높고 초기설치비용이 저렴하며 국내 지반 수리조건에 적합하다. 반면, 국내에는 아직 SCW의 성능을 평가할 수 있는 수치해석 모델이 없으며 국내 자료를 적용한 수치해석이 수행된 바 없다. 본 연구에서는 SCW 수치해석 모델을 유한체적해석 프로그램을 이용하여 구축하였다. 수치적 모델은 수리 열 연계해석을 수행하여 열이류, 대류, 전도를 모두 모사한다. SCW 모델은 미국과 국내에서 계측된 현장 데이터를 통하여 검증하였다. 비교 결과 본 연구에서 구축된 수치해석 모델은 정확하게 SCW의 거동을 예측할 수 있는 것으로 나타났다. 검증된 수치해석 모델은 동반논문에서 매개변수연구에 활용되었다.

• 한국정보교육학회:학술대회논문집
• /
• 한국정보교육학회 2021년도 학술논문집
• /
• pp.123-129
• /
• 2021

최근 인공지능 교육을 위한 다양한 플랫폼과 컨텐츠가 개발되고 있지만, 초등 저학년 학습자를 위한 인공지능 교육 프로그램은 미비한 상황이다. 이에 본 연구는 초등학교 저학년 학습자를 위한 인공지능 교육 프로그램을 개발하는 것을 목적으로 한다. 이를 위해 노벨 엔지니어링 기법을 활용하여 교육 프로그램을 설계하였고 전문가 타당도 검사로 타당도를 검증하였다. 그 결과 한글 해득 과정 중인 저학년 학습자 수준을 고려하여 문자 언어보다는 음성 언어를 기반으로 한 프로그램을 구성하고, 교과 간 통합으로 교육 시수를 확보할 필요가 있었다. 이를 반영하여 정보 교과가 별도로 신설되어 있지 않은 초등 저학년의 교육과정을 고려하여 국어, 수학, 통합 교과와 융합하여 인공지능 교육 프로그램을 설계하였다. 노벨 엔지니어링은 그동안 소프트웨어 교육을 위한 다양한 융합교육 연구사례가 있었고 그 효과가 검증되었다. 학습의 풍부한 맥락을 제공하여 주는 노벨 엔지니어링을 통해 초등 저학년을 위한 인공지능교육의 새로운 방향성을 제시할 수 있을 것으로 기대한다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2020년도 학술발표회
• /
• pp.172-172
• /
• 2020

In recent years, soil erosion has come to be regarded as an essential environmental problem in human life. Soil erosion causes various on- and off-site problems such as ecosystem destruction, decreased agricultural productivity, increased riverbed deposition, and deterioration of water quality in streams. To solve these problems caused by soil erosion, it is necessary to quantify where, when, how much soil erosion occurs. Empirical erosion models such as the Universal Soil Loss Equation (USLE) family models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well by utilizing big data related to climate, geography, geology, land use, etc. within study domains, they do not adequately describe the physical process of soil erosion on the ground surface caused by rainfall or overland flow. In other words, such models remain powerful tools to distinguish erosion-prone areas at the macro scale but physics-based models are necessary to better analyze soil erosion and deposition and eroded particle transport. In this study, the physics-based Surface Soil Erosion Model (SSEM) was upgraded based on field survey information to produce sediment yield at the watershed scale. The modified model (hereafter MoSE) adopted new algorithms on rainfall kinematic energy and surface flow transport capacity to simulate soil erosion more reliably. For model validation, we applied the model to the Doam dam watershed in Gangwon-do and compared the simulation results with the USLE outputs. The results showed that the revised physics-based soil erosion model provided more improved and reliable simulation results than the USLE in terms of the spatial distribution of soil erosion and deposition.

• 천문학회보
• /
• 제44권2호
• /
• pp.78.1-78.1
• /
• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.